Retail supply chain management system

ABSTRACT

The present supply chain management system has an e-commerce subsystem having a product inventory database comprising product SKU and pricing data and an e-commerce frontend interfacing the product inventory database for receiving retailer e-commerce orders. The system also has an order management subsystem having an aggregation controller for aggregating the retailer e-commerce orders into supply orders, an aggregation optimiser for optimising the supply orders; and an order dispatch controller for dispatching the supply orders to suppliers. The system also has an electronic warehouse infrastructure having a pick grid controller having product tracking electronic scanning devices, the pick grid controller configured for generating pick grid instructions for pick-to-zero product placement from supplier pallets received for the supply orders to order pallets configured according to the retailer e-commerce orders.

FIELD OF THE INVENTION

This invention relates to electronic retail supply chain managementsystems.

BACKGROUND OF THE INVENTION

Retail supply chain management systems are used for tracking dispatchand delivery of retail goods in a supply chain from suppliers toretailers.

Prior art retail supply management systems are generally “push ordering”systems which employ warehouse stockpiling to optimise delivery timesand fulfil demand. Such prior art push ordering systems may compriseelectronic warehouse infrastructure to control the routing of productsthrough a pick grid wherein supplier products are stockpiled on racksand recorded against rack locations such that, when fulfilling retailerorders, the system controls the movement of order pallet between theracks for picking and electronically scanning the requisite orderedgoods from the racks until the orders are fulfilled

For example, WO 2016/081794 A1 (DELIVERIGHT LOGISTICS, INC.) 26 May 2016[hereinafter referred to as D1] discloses a conventional push orderingsystem delivery management system. D1 is characterised in managingquality between distributors and manufacturers by using an inspectioncomponent (which can, for example identify potential damage to a goodbased on image analysis) configured to require inspection of items ateach of the plurality of delivery events (e.g., pick up, freightaggregation, line shipping, local shipping, etc.).

Furthermore, US 2009/0112675 A1 (SERVAIS) 30 Apr. 2009 [hereinafterreferred to as D2] discloses a conventional “push ordering” system. D2is characterised in seeking to manage product quantities whileconcurrently tracking the quantities of the product throughout allprocesses of the system.

However, such prior art push ordering retail supply chains, whilst beingable to minimise delivery times and meet customer demand, have problemsassociated with inventory stockpiling wherein, for example, stock heldfor too long may spoil and conversely, insufficient stock levels mayresult in delivery delays.

As such, such prior art push ordering retail supply chains generallyseek to optimise warehouse inventory stock levels to address theseproblems.

It is to be understood that, if any prior art information is referred toherein, such reference does not constitute an admission that theinformation forms part of the common general knowledge in the art, inAustralia or any other country.

SUMMARY OF THE DISCLOSURE

There is provided herein a supply chain management system and, moreparticularly, to a reversed “pull ordering” tracking retail supply chainmanagement system. The present electronic retail supply chain managementsystem intelligently aggregates retailer e-commerce orders intooptimised supplier orders in real time in response to actual orpredicted retail order demand and/or dynamic supply chain managementparameters for pick-to-zero warehouse management product electronicscanning and picking from supplier pallets, received for the supplyorders, to order pallets, configured according to the retailere-commerce orders, to adaptively avoid or minimise inventorystockpiling, minimise the number of orders, minimise product cost,minimise delivery cost and/or optimise delivery times.

Specifically, the present supply chain management comprises threeprimary interoperating electronic subsystems comprising an e-commercesubsystem for controlling a plurality of e-commerce orders for aplurality of product SKUs, an order management system comprising anaggregation controller for controlling the aggregation of the pluralityof e-commerce orders into a plurality of supplier orders and electronicwarehouse infrastructure comprising a pick grid controller in operablecommunication with a plurality of electronic scanning devices, operableto, upon receipt of inbound supplier pallets for the supplier orders, tocontrol the pick-to-zero picking of products from the supplier palletsto fixed order pallets

The present e-commerce subsystem may comprise a product inventorydatabase comprising product SKU and pricing data stored therein. Thee-commerce subsystem may comprise an API for real time supplier pricingdata modification and an e-commerce front-end API for ordering ofproducts from the inventory database by retailers.

Furthermore, the present order management subsystem performs orderaggregation dispatch control processing wherein a plurality ofe-commerce orders for a plurality of product SKUs are optimised into aplurality of supply orders which are dispatched to suppliers using asupplier order dispatch controller.

Furthermore, the present pick grid controller differs from prior artpick grid controllers in that the present pick grid controller optimisespick-to-zero processing from supplier pallets (typically mobile) toorder pallets (typically fixed) as opposed to prior art systems whichcontrol the movement of order pallets between static stockpile racks.Specifically, the present pick grid controller is configured for readingdata from electronic scanning devices for tracking supplier pallets,order parts and product SKUs for controlling the pick-to-zero placementof products on fixed order pallets from the mobile supplier palletsreceived for the optimised supply orders.

Furthermore, the present system may comprise inbuilt optimiser modulesfor intelligently optimising aggregation of supplier orders, picking ofproducts and dispatch of order pallets.

Specifically, the order management subsystem may comprise an aggregationoptimiser for optimising the operation of the aggregation controller tooptimise supply orders and the dispatch thereof.

Furthermore, the electronic warehouse infrastructure may comprise a pickgrid optimiser for optimising the pick grid controller.

Furthermore, the electronic warehouse infrastructure may comprise adispatch control for optimising the dispatching of order pallets.

These optimiser modules may implement machine learning wherein a machinelearning module is trained with training data for the generation ofoptimised parameters which are then used to control a trained/optimisedsystem so as to intelligently and dynamically adapt to nuances andchanges in pull demand supply chain variables.

As can be appreciated from the above, the present supply chainmanagement system is characterised in several respects from prior artpush ordering systems such as D1 and D2 above including in comprisingthe aggregation controller and optimiser which aggregates retailere-commerce orders into optimised supply orders which are dispatched tosuppliers. As such, the present aggregation controller and optimiser mayoptimise the minimisation of inventory levels, number of orders, productand delivery costs and/or optimised delivery time by product type.

Furthermore, the present supply chain management system is characterisedin comprising the electronic warehouse infrastructure comprising thepick grid controller and optimiser configured for optimisingpick-to-zero product placement from mobile supplier pallets for theoptimised supply orders to fixed order pallets configured according tothe retailer e-commerce orders.

According to one aspect, there is provided a supply chain managementsystem comprising: an e-commerce subsystem comprising: a productinventory database comprising product SKU and pricing data; ane-commerce frontend interfacing the product inventory database forreceiving retailer e-commerce orders; an order management subsystemcomprising: an aggregation controller for aggregating the retailere-commerce orders into supply orders; an aggregation optimiser foroptimising the supply orders; an order dispatch controller fordispatching the supply orders to suppliers; electronic warehouseinfrastructure comprising: a pick grid controller having producttracking electronic scanning devices, the pick grid controllerconfigured for generating pick grid instructions for pick-to-zeroproduct placement from supplier pallets received for the supply ordersto order pallets configured according to the retailer e-commerce orders.

The pick grid controller may be configured for receiving at least one ofproduct identifier, supplier pallet ID and order pallet ID data from theproduct tracking electronic scanning devices.

The pick grid controller may be configured for generating the pick gridinstructions for manoeuvring the supplier pallets between fixed orderpallets within the warehouse.

The supply chain management system may further comprise a pick gridoptimiser for optimising the pick grid instructions.

The pick grid optimiser may be configured for at least one of minimisingthe number of picks, optimising packing of the order pallets inaccordance with product type, minimising the number of order pallets andoptimising packing of the order pallets in accordance with productpriority type.

The supply chain management system may further comprise a deliverydispatch controller for controlling dispatch of the order pallets.

The pick grid controller may be operably coupled to the deliverydispatch controller for generating the pick grid instructions accordingto dispatching of the order pallets.

The supply chain management system may further comprise a deliverydispatch optimiser for optimising the dispatch of the order pallets.

The delivery dispatch optimiser may be configured for at least one ofminimising the number of deliveries, minimising delivery time,minimising delivery distances, minimising delivery cost and delivery ofpriority goods.

The e-commerce subsystem further may comprise an API interface forupdating the pricing data.

The aggregation optimiser may comprise a machine learning moduleconfigured for generating optimising parameters in accordance withtarget parameters and historical data and wherein the optimisingparameters may be used to optimise the supply orders in accordance withaggregation target parameters comprising at least one of minimisinginventory levels, minimising the number of orders, minimising productcost, minimising delivery cost and optimising delivery time by producttype.

The pick grid optimiser may comprise a machine learning moduleconfigured for generating optimising parameters in accordance withtarget parameters and historical data and wherein the optimisingparameters may be used to optimise the pick grid instructions inaccordance with pick grid target parameters comprising at least one ofminimising the number of picks, optimising the packing of the orderpallets in accordance with product type, minimising the number of orderpallets and optimising priority products.

The delivery dispatch optimiser may comprise a machine learning moduleconfigured for generating optimising parameters in accordance withtarget parameters and historical data and wherein the optimisingparameters may be used to optimise wherein the dispatch instructions inaccordance with dispatch target parameters comprising at least one ofminimising the number of deliveries, minimising delivery time,minimising delivery distances, minimising delivery cost and optimisingpriority goods.

Other aspects of the invention are also disclosed.

BRIEF DESCRIPTION OF THE DRAWINGS

Notwithstanding any other forms which may fall within the scope of thepresent invention, preferred embodiments of the disclosure will now bedescribed, by way of example only, with reference to the accompanyingdrawings in which:

FIG. 1 shows a supply chain management system in accordance with anembodiment;

FIG. 2 shows exemplary processing performed by the system of FIG. 1;

FIG. 3 shows an aggregation optimiser in accordance with an embodiment;

FIG. 4 shows a pick grid optimiser in accordance with an embodiment; and

FIG. 5 shows a dispatch optimiser in accordance with an embodiment.

DESCRIPTION OF EMBODIMENTS

FIG. 1 shows a supply chain management system 1 being a technicalimplementation for pull demand retail supply chain retail producttracking and optimisation.

The supply chain management system 1 is illustrated in FIG. 1 byfunctional demarcation for illustrative convenience. As such, it shouldbe appreciated that the supply chain management system 1 may beimplemented by a standalone computing devices/servers or various networktopologies of interconnected computing devices/servers for implementingthe technical functionality described herein.

Each constituent computing device may comprise a processor forprocessing digital data and a memory device for storing digital dataincluding computer program code instructions in operable communicationwith the memory device. As such, the various computer processingfunctionality described herein may be embodied in computer program codemodules for the various processes, which may be fetched by the processorfor execution.

In the embodiment shown in FIG. 1, the computer componentry has beendemarcated into three primary technical subsystems being the e-commercesubsystem 2, order management subsystem 3 and the electronic warehouseinfrastructure 4.

The e-commerce subsystem 2 comprises an inventory database 6 which maycomprise product SKU and pricing data.

The product SKU data may be populated using an SKU database, such as theAustralian GS1 product SKU database 11.

The e-commerce subsystem 2 may comprise a product data API 5 exposing aninterface which may be used by suppliers in updating product pricingdata within the database 6. Specifically, various suppliers, usingassociated supplier client computing devices 10 may authenticate withthe API 5 for such purposes.

Furthermore, the e-commerce subsystem 2 described herein may be furthercharacterised in comprising a retailer e-commerce front-end 7 and API 7Awherein retailers, similarly using associated retailer client computerdevices 12 may place a plurality of e-commerce orders for a plurality ofproduct SKUs.

The system 1 further comprises an order management subsystem 3technically configured for the aggregation of supply orders using theplurality of e-commerce orders.

Specifically, the order management subsystem 3 comprises an aggregationcontroller 14 configured for aggregating the e-commerce orders 13 into aplurality of supply orders 15. Specifically, in general terms, theaggregation controller 14 at least groups product SKU orders by supplierin generating the supply orders 15. In doing so, the aggregationcontroller 14 may extract data from the inventory database 6 so as tocross-reference product SKU and supplier related data for such purposes.

As alluded to above, the order management subsystem 3 may furthercomprise an aggregation optimiser 8 for optimising the aggregationprocess in the manner described in further detail below. As such, theaggregation optimiser 8 is configured for optimising the generation ofthe supply orders 15.

Having generated the optimised supply orders 15, the order managementsubsystem 3 may comprise an order controller 20 configured for placingorders with suppliers. Specifically, the order controller 20 mayinterface with a plurality of supplier APIs 19 so as to be able toelectronically place such optimised supply orders 15 via associatedsupplier servers.

The system 1 further comprises electronic warehouse infrastructure 4comprising warehouse storage and associated electronic trackinginfrastructure for receiving supplier pallets and controlling the pickgrid “pick-to-zero process” when loading order pallets.

Specifically, the electronic warehouse infrastructure 4 comprises a pickgrid controller 18 in operable communication with a plurality ofelectronic scanning devices 21. The electronic scanning devices 21 maytake the form of small form factor handheld electronic scanning devicesable to read barcodes or other computer readable media.

For a supplier pallet arriving at the warehouse, the supplier pallet maycomprise a supplier pallet ID barcode or a plurality of supplier productIDs for each of or a subset group of the products thereon.

The electronic scanning infrastructure of the electronic warehouseinfrastructure 4 is distinguished in that the pick grid controller 18 isconfigured for tracking for the picking of products from fixed ormovable supplier pallets to fixed order pallets (wherein the fixed orderpallets may be configured in accordance with e-commerce orders receivedvia the e-commerce front-end 7 or API 7 a) as opposed to prior artelectronic warehouse infrastructures which are configured for trackingfor the picking of products from fixed stockpile racks to movable orderpallets.

As such, for such handheld electronic scanning devices 21, operators mayscan the supplier pallets, product SKUs and order pallets so as to allowthe pick grid controller 18 to direct the picking process as productsare picked from the supplier pallets to the order pallets.

The operation of the pick grid controller 18 may be optimised using apick grid optimiser 9 as will be described in further detail below. Thepick grid optimiser 9 is configured for optimising the picking processin accordance with the various parameters.

There electronic warehouse infrastructure 4 further comprises a dispatchcontroller 17 for configuring the dispatch of the packed order pallets.

The dispatch controller 17 may read data from the electronic scanningdevices 21 as the order pallets are dispatched and transportationnetwork APIs 22 for the controlling of the dispatch of the packed orderpallets.

A dispatch optimiser 16 may be used for optimising the dispatchcontroller 17 in the manner described in further detail below, includingin accordance with data received via the transportation network APIs 22.

Turning now to FIG. 2, there is shown exemplary processing 23 of thesupply chain management system 1 for illustrating various technicalfunctionality thereof.

The processing 23 may comprise the population 24 of the productinventory database 6 with product inventory data including product SKUand product pricing data. The product inventory database 6 may furthercomprise supplier data stored in relation to the product SKU data usedby the aggregation controller 14 for resolving the e-commerce orders 13into optimised supply orders 15.

The processing 23 may comprise receiving periodic pricing updates 25 viathe API wherein suppliers, using associated supplier client computerdevices 10 may authenticate with the API 5 for updating the pricing dataassociated with the product SKU data stored within the product inventorydatabase 6. Such updating 26 may further comprise the incorporation ofnew product SKU data for new products.

Having the product inventory database 6 comprising product SKUs andpricing data stored in relation to the product SKUs, the processing mayfurther comprise the e-commerce frontend 7 and API 7 a receiving 26 aplurality of e-commerce orders for a plurality of product SKUs from aplurality of retailers using a plurality of associated retailer clientcomputing devices 12.

The e-commerce front-end 7 may further facilitate payment for thee-commerce orders.

The API 7 a may further facilitate the issuing of electronic invoicesfor the e-commerce orders.

The e-commerce order data received via the e-commerce front-end 7 or API7A are then conveyed to the e-commerce orders database 13 wherein theprocessing 23 further comprises aggregating 27 the plurality ofe-commerce orders 13 into a plurality of supplier orders 15.

The aggregation 27 comprises the aggregation controller 14 aggregatingproduct SKUs by suppliers with reference to the inventory database 6.

The aggregation 27 may further comprise the optimisation of the supplyorders 15 to generate optimised supply orders 15 using the aggregationoptimiser 8 in the manner described in further detail below.

The optimised supply orders 15 are then conveyed to the order controller20 wherein the processing 23 further comprises the dispatch 28 of theoptimised supply orders using the order controller 20 which may utilisesupplier server APIs 19.

The processing 23 may further comprise the receipt 29 of a plurality ofsupplier pallets at the warehouse pick grid. Each supplier pallet maycomprise a plurality of products of each supplier. Typically, thewarehouse pick grid may comprise a plurality of order pallets laid outaccording to the e-commerce orders and wherein the processing 23 furthercomprise “pick-to-zero” product picking wherein products are picked fromthe supplier pallets for placement on the order pallets in the mannerdirected by the pick grid controller 18.

The pick-to-zero process 30 may further utilise the pick grid optimiser9 for optimising the pick-to-zero process in the manner described infurther detail below.

The processing 23 may further comprise the control of the dispatch ofthe order pallets 31 using the dispatch controller 17 which may readdata from the electronic scanning devices 21 as order pallets aredispatched and transportation network data received via transportationnetwork APIs 22.

The control of the dispatch of order pallets 31 may be optimised usingthe dispatch optimiser 16 in the manner described in further detailbelow.

The pick grid optimiser 9 of the electronic warehouse infrastructure mayinteract with the dispatch optimiser 16 to optimise picking inaccordance with dispatch requirements.

Turning now to FIGS. 3-5 there is shown the optimisation process of thevarious optimisation modules comprising the aggregation optimiser 8shown in FIG. 3, the pick grid optimiser 9 shown in FIG. 4 and thedispatch optimiser 16 shown in FIG. 5.

In embodiments, the optimisation process may utilise machine learning.In this embodiment, the process involves the utilisation of a machinelearning module 33 having as input training data 32, 37, 38 configuredfor generating optimising parameters 34 for optimising various targetparameters 39-41.

The machine learning module 33, when optimising the various targetparameters 39-41 and using the training data 32, 37, 38 may generateoptimising parameters 34 which are then used to configure the optimisedcontrollers, being the optimised aggregation controller 14, optimisedpick grid controller 18 and optimised dispatch controller 17 for thegeneration of optimised results in accordance with real-time data 42,comprising, in this case, the optimised supply orders 15, optimised pickgrid instructions 35 and optimised dispatch instructions 36respectively.

Considering initially the aggregation optimiser 8 shown in FIG. 3, theaggregation target parameters 39 may comprise one or more of aggregationtarget parameters to minimise the number of orders placed, minimise theproduct cost, minimise the delivery cost and optimise delivery time byproduct type. It should be noted that the machine learning module 33 mayoptimise the optimising parameters 34 in accordance with a combinationof these parameters wherein, for example, there is a trade-off betweenminimising the number of orders placed and minimising product cost.

In one example, wherein the aggregation target parameters 39 includes atarget parameter to optimise delivery time by product type, the machinelearning module 33 may seek to optimise the delivery of time sensitiveor necessity priority retail products, such as bottled water as opposedto other retail products such as floor mops or the like.

As such, the optimised supply orders 15 generated in this manner may,for the given product data, supplier data and the like, generateoptimise supply orders 15 that seek to minimise, for example, the numberof orders placed, minimise the product cost, minimise the delivery costand optimise the delivery of priority retail products.

The embodiment shown in FIG. 4 shows the pick grid optimiser 9. In asimilar manner as provided above, the pick grid optimiser 9 may beconfigured for optimising the pick-to-zero pick grid process inaccordance with various pick grid target parameters 40.

For example, the pick grid target parameters 40 may comprise targetparameters to minimise the number of picks (i.e. minimise movement aboutthe warehouse), optimise the packing of pallets wherein, for example,heavy items are placed first and lighter and/or more fragile items orloose items placed thereafter on top, minimise the number of palletsrequired to fulfil orders, optimise priority products wherein priorityproducts are allocated to pallets for priority dispatch and the like.

The training data 37 may comprise historical pallet data, supplytransportation network data, transportation network data, product dataand the like.

As such, the optimised pick grid controller 18 may therefore, inaccordance with the real-time data 42, optimise the target parameters 40resulting in optimised pick grid instructions 35.

The embodiment shown in FIG. 5 shows the dispatch optimiser 16 wherein,similarly, the dispatch optimiser 16 may generate optimised dispatchinstructions 16 optimising various target parameters.

For example, the dispatch target parameters 41 may comprise targetparameters to minimise the number of deliveries, minimise the deliverytime, minimise delivery distances, minimise delivery cost, prioritisenecessary goods and the like.

The machine learning module 33 may take into account pallet data, suchas the locations of contents of pallets ready for dispatch, informationrelating to the retail transportation network data, product data and thelike.

As such, the optimised dispatch controller 17, having the real-time data42, may generate optimised dispatch instructions 36 taking into accountinformation relating to the retail transportation network data, palletdata and product contents thereof.

As is shown in FIG. 1, the optimisers may work in conjunction wherein,for example, when prioritising priority goods, the aggregation optimiser8 may take into account supply transportation network data indicative ofthe locations of the various suppliers wherein, for example, orders maybe placed to transport supplier products to the closest warehouses 4 tominimise delivery times and wherein, at those warehouses 4, the pickgrid optimiser 9 may furthermore prioritise the picking of thosenecessary goods to pallets for priority dispatch and wherein, forexample, the dispatch optimiser 16 may then generate optimised dispatchinstructions 36 to minimise the delivery of the necessary goods via theretail transportation network.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the invention.However, it will be apparent to one skilled in the art that specificdetails are not required in order to practice the invention. Thus, theforegoing descriptions of specific embodiments of the invention arepresented for purposes of illustration and description. They are notintended to be exhaustive or to limit the invention to the precise formsdisclosed; obviously, many modifications and variations are possible inview of the above teachings. The embodiments were chosen and describedin order to best explain the principles of the invention and itspractical applications, they thereby enable others skilled in the art tobest utilize the invention and various embodiments with variousmodifications as are suited to the particular use contemplated. It isintended that the following claims and their equivalents define thescope of the invention.

1. A supply chain management system comprising: an e-commerce subsystemcomprising: a product inventory database comprising product SKU andpricing data; and an e-commerce frontend interfacing the productinventory database for receiving retailer e-commerce orders; an ordermanagement subsystem comprising: an aggregation controller foraggregating the retailer e-commerce orders into supply orders; anaggregation optimiser for optimising the supply orders; and an orderdispatch controller for dispatching the supply orders to suppliers;electronic warehouse infrastructure comprising: a pick grid controllerhaving product tracking electronic scanning devices, the pick gridcontroller configured for generating pick grid instructions forpick-to-zero product placement from supplier pallets received for thesupply orders to order pallets configured according to the retailere-commerce orders.
 2. A supply chain management system as claimed inclaim 1, wherein the pick grid controller is configured for receiving atleast one of product identifier, supplier pallet ID and order pallet IDdata from the product tracking electronic scanning devices.
 3. A supplychain management system as claimed in claim 1, wherein the pick gridcontroller is configured for generating the pick grid instructions formanoeuvring the supplier pallets between fixed order pallets within thewarehouse.
 4. A supply chain management system as claimed in claim 1,further comprising a pick grid optimiser for optimising the pick gridinstructions.
 5. A supply chain management system as claimed in claim 4,wherein the pick grid optimiser is configured for at least one ofminimising the number of picks, optimising packing of the order palletsin accordance with product type, minimising the number of order palletsand optimising packing of the order pallets in accordance with productpriority type.
 6. A supply chain management system as claimed in claim1, further comprising a delivery dispatch controller for controllingdispatch of the order pallets.
 7. A supply chain management system asclaimed in claim 6, wherein the pick grid controller is operably coupledto the delivery dispatch controller for generating the pick gridinstructions according to dispatching of the order pallets.
 8. A supplychain management system as claimed in claim 6, further comprising adelivery dispatch optimiser for optimising the dispatch of the orderpallets.
 9. A supply chain management system as claimed in claim 8,wherein the delivery dispatch optimiser is configured for at least oneof minimising the number of deliveries, minimising delivery time,minimising delivery distances, minimising delivery cost and delivery ofpriority goods.
 10. A supply chain management system as claimed in claim1, wherein the e-commerce subsystem further comprises an API interfacefor updating the pricing data.
 11. A supply chain management system asclaimed in claim 1, wherein the aggregation optimiser comprises amachine learning module configured for generating optimising parametersin accordance with target parameters and historical data and wherein theoptimising parameters are used to optimise the supply orders inaccordance with aggregation target parameters comprising at least one ofminimising inventory levels, minimising the number of orders, minimisingproduct cost, minimising delivery cost and optimising delivery time byproduct type.
 12. A supply chain management system as claimed in claim4, wherein the pick grid optimiser comprises a machine learning moduleconfigured for generating optimising parameters in accordance withtarget parameters and historical data and wherein the optimisingparameters are used to optimise the pick grid instructions in accordancewith pick grid target parameters comprising at least one of minimisingthe number of picks, optimising the packing of the order pallets inaccordance with product type, minimising the number of order pallets andoptimising priority products.
 13. A supply chain management system asclaimed in claim 6, wherein the delivery dispatch optimiser comprises amachine learning module configured for generating optimising parametersin accordance with target parameters and historical data and wherein theoptimising parameters are used to optimise wherein the dispatchinstructions in accordance with dispatch target parameters comprising atleast one of minimising the number of deliveries, minimising deliverytime, minimising delivery distances, minimising delivery cost andoptimising priority goods.